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Various intracellular degradation organelles, including autophagosomes, lysosomes, and endosomes, work in tandem to perform autophagy, which is crucial for cellular homeostasis. Altered autophagy contributes to the pathophysiology of various diseases, including cancers and metabolic diseases. This paper aims to describe an approach to reproducibly identify and distinguish subcellular structures involved in macroautophagy. Methods are provided that help avoid common pitfalls. How to distinguish between lysosomes, lipid droplets, autolysosomes, autophagosomes, and inclusion bodies are also discussed. These methods use transmission electron microscopy (TEM), which is able to generate nanometer-scale micrographs of cellular degradation components in a fixed sample. Serial block face-scanning electron microscopy is also used to visualize the 3D morphology of degradation machinery using the Amira software. In addition to TEM and 3D reconstruction, other imaging techniques are discussed, such as immunofluorescence and immunogold labeling, which can be used to classify cellular organelles, reliably and accurately. Results show how these methods may be used to accurately quantify cellular degradation machinery under various conditions, such as treatment with the endoplasmic reticulum stressor thapsigargin or ablation of the dynamin-related protein 1. 

We apply the color–magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric data set. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the linear region in the color–magnitude diagram, which occurs over a span of approximately 30 days after maximum brightness. We define a new parameter,ω_XY, the size of the “bump” feature near maximum brightness for arbitrary filtersXandY. We find a significant correlation between the slope of the linear region,β_XY, in the CMAGIC diagram andω_XY. These results may be used to our advantage, as they are less affected by extinction than parameters defined as a function of time. Additionally,ω_XYis computed independently of templates. We find that current empirical templates are successful at reproducing the features described in this work, particularly SALT3, which correctly exhibits the negative correlation between slope and “bump” size seen in our data. In 1D simulations, we show that the correlation between the size of the “bump” feature andβ_XYcan be understood as a result of chemical mixing due to large-scale Rayleigh–Taylor instabilities.

 

Abstract We construct a physically parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of Type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an autoencoder that is interpreted probabilistically after training using a normalizing flow. We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multistage training setup alongside our physically parameterized network, we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including the automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an rms of 0.091 ± 0.010 mag, which corresponds to 0.074 ± 0.010 mag if peculiar velocity contributions are removed. Trained models and codes are released at https://github.com/georgestein/suPAErnova. 

We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the Hubble Space Telescope–based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300–9400 Å as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standard-star spectra taken on photometric nights. As a modern cosmology analysis, all presubmission methodological decisions were made with the flux scale and external comparison results blinded. The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. We determine the typical per-observation repeatability (median 14 mmag for exposures ≳5 s), the Maunakea atmospheric transmission distribution (median dispersion of 7 mmag with uncertainty 1 mmag), and the scatter internal to our CALSPEC reference stars (median of 8 mmag). We also check our standards against literature filter photometry, finding generally good agreement over the full 12 mag range. Overall, the mean of our system is calibrated to the mean of CALSPEC at the level of ∼3 mmag. With our large number of observations, careful cross-checks, and 14 reference stars, our results are the best calibration yet achieved with an integral-field spectrograph, and among the best calibrated surveys.

 

Abstract Several improvements to the ATLAS triggers used to identify jets containing b -hadrons ( b -jets) were implemented for data-taking during Run 2 of the Large Hadron Collider from 2016 to 2018. These changes include reconfiguring the b -jet trigger software to improve primary-vertex finding and allow more stable running in conditions with high pile-up, and the implementation of the functionality needed to run sophisticated taggers used by the offline reconstruction in an online environment. These improvements yielded an order of magnitude better light-flavour jet rejection for the same b -jet identification efficiency compared to the performance in Run 1 (2011–2012). The efficiency to identify b -jets in the trigger, and the conditional efficiency for b -jets that satisfy offline b -tagging requirements to pass the trigger are also measured. Correction factors are derived to calibrate the b -tagging efficiency in simulation to match that observed in data. The associated systematic uncertainties are substantially smaller than in previous measurements. In addition, b -jet triggers were operated for the first time during heavy-ion data-taking, using dedicated triggers that were developed to identify semileptonic b -hadron decays by selecting events with geometrically overlapping muons and jets. 

Abstract The production cross-section of a top quark in association with a W boson is measured using proton–proton collisions at $$\sqrt{s} = 8\,\text {TeV}$$ s = 8 TeV . The dataset corresponds to an integrated luminosity of $$20.2\,\text {fb}^{-1}$$ 20.2 fb - 1 , and was collected in 2012 by the ATLAS detector at the Large Hadron Collider at CERN. The analysis is performed in the single-lepton channel. Events are selected by requiring one isolated lepton (electron or muon) and at least three jets. A neural network is trained to separate the tW signal from the dominant $$t{\bar{t}}$$ t t ¯ background. The cross-section is extracted from a binned profile maximum-likelihood fit to a two-dimensional discriminant built from the neural-network output and the invariant mass of the hadronically decaying W boson. The measured cross-section is $$\sigma _{tW} = 26 \pm 7\,\text {pb}$$ σ tW = 26 ± 7 pb , in good agreement with the Standard Model expectation.